Modeling socio-ecological interactions are one of the essential requirements for water resource management in water-stressed areas. Mismanagement of water resource combined with extensive withdrawal by farmers in Indus Basin is putting pressure on freshwater resources. In some areas sever depletion of groundwater is evident. Waterlogging remains a bigger problems in the areas with higher surface water endowments, causing salinization; a greatest threat to long-term groundwater sustainability. Physical water management solution wouldn’t be a successful approach as it ignores water users’ behaviors; their interaction with each other and the feedback effects they receive from the system. We have developed an ABM model simulating the system by varying different agro-climatic parameters for water withdrawal behaviours of framers to substantiate a groundwater development framework in conjunction with the management of surface water. Overtime spatially distributed farmers’ caricaturized scenarios were built to include groundwater depth fluctuations for better management of water resources. Self-governing Rules (SGR) and Institutional Management Perspective(IMP) bring equity in water availability and prevent agriculture from worsening water quality parameters. However, consistency in the benefits may break down in extreme cases of climate change and spatio-physical conditions. Our water management perspectives provide improved outcomes of water withdrawal. SGR perspective managed to increase groundwater abstraction price 3 times more than the existing rates for the farmers located near water source. For the farmers located at tails IMP appears to manage resource better than other scenarios. Better and sustainable water withdrawal management requires to have area-wise policies and institutional support for promotion of norms.

We present long-term density trends of the Earth’s upper atmosphere at altitudes between 71 and 116 km, based on atmospheric occultations of the Crab Nebula observed with X-ray astronomy satellites, ASCA, RXTE, Suzaku, NuSTAR, and Hitomi. The combination of the five satellites provides a time period of 28 yr from 1994 to 2022. To suppress seasonal and latitudinal variations, we concentrate on the data taken in autumn (49 < doy < 111) and spring (235 < doy < 297) in the northern hemisphere with latitudes of 0◦–40◦. With this constraint, local times are automatically limited either around noon or midnight. We obtain four sets (two seasons × two local times) of density trends at each altitude layer. We take into account variations due to a linear trend and the 11-yr solar cycle using linear regression techniques. Because we do not see significant differences among the four trends, we combine them to provide a single vertical profile of trend slopes. We find a negative density trend of roughly −5%/decade at every altitude. This is in reasonable agreement with inferences from settling rate of the upper atmosphere. In the 100–110 km altitude, we found an exceptionally high density decline of about −12%/decade. This peak may be the first observational evidence for strong cooling due to water vapor and ozone near 110 km, which was first identified in a numerical simulation by Akmaev et al. (2006). Further observations and numerical simulations with suitable input parameters are needed to establish this feature.

In the field of geophysical exploration, fault identification is an important part. However, Due to the fixed identification mode, conventional computing methods cannot adapt to complex fault characteristics and environmental noise; And, the convolution neural network is prone to loss of global information because the slow convergence of global information. For those reasons, the previous fault identification methods cannot achieve higher resolution and accuracy fault identification, such as the identification results are very coarse, the identification results are inaccurate and results are discontinuous, especially in the case of severe environmental noise and complex fault interlacing. So in order to solve these problems, we proposed a deep learning network based on Transformer with improved window mechanism. Starting from the features of the data itself, quickly gather global information, and results on field data show that our network effectively solve the above problems. This work has positive significance for high-precision exploration work.

Many of the factors expected to control the dynamics and evolution of Earth’s subduction zones are under-explored in an Earth-like spherical geometry. Here, we simulate multi-material free-subduction of a complex rheology slab in a 3-D spherical shell domain, to investigate the effect of plate age (simulated by covarying plate thickness and density) and width on the evolution of subduction systems. We find that the first-order predictions of our spherical cases are generally consistent with existing Cartesian studies: (i) as subducting plate age increases, slabs retreat more and subduct at a shallower dip angle, due to increased bending resistance and sinking rates; and (ii) wider slabs can develop along-strike variations in trench curvature due to toroidal flow at slab edges, trending towards a ‘W’-shaped trench with increasing slab width. We find, however, that these along-strike variations are restricted to older, stronger, retreating slabs:. Younger slabs that drive minimal trench motion remain relatively straight along the length of the subduction zone. We summarise our results into a regime diagram, which highlights how slab age modulates the effect of slab width, and present examples of the evolutionary history of subduction zones that are consistent with our model predictions.

The barotropic vorticity balance is fundamental to interpret the ocean gyre circulation. However, it remains an overlooked diagnostic in ocean circulation models. Here we propose a general theory of barotropic vorticity balances for use in ocean models. We show that there exist four distinct barotropic vorticity balances, each giving access to diagnostic equations for the depth-integral ocean circulation, either meridional, across geostrophic contours or its divergence. We then formulate those balances in the Vorticity Balances in NEMO (VoBiN) diagnostic package aimed at the NEMO ocean platform and more generally C-grid ocean models. We show that space discretization of the equations of motion have profound implications for those vorticity balances. Finally, we diagnose the main balances of a global ocean climate simulation. In all vorticity balances, topographic torques, both physical and numerical, arise from interactions of the flow with slanting topography. Contrary to Sverdrup theory, the wind stress curl, although dominant in the interior Subtropics, becomes a minor player anywhere significant bottom velocities prevail. Depth-dependent vorticity dynamics show that the barotropic balance emerges from the coupling of the surface (frictional) and bottom (topographic) boundary layers to the geostrophic interior through the generation of vertical motion. The vorticity balance for the depth-averaged momentum equations highlights the limits of barotropic models of the ocean circulation. Finally, the vorticity balance for the transport divergence highlights the key role played by numerical torques for the oceanic mass balance. This framework should encourage ocean modellers to diagnose more routinely momentum and vorticity equations.

A secondary zone of surface uplift (SZU), located ~300 kilometers from the trench, has been measured after several megathrust earthquakes. The SZU reached a few centimeters hours after the 2011 Mw 9.1 Tohoku (Japan) earthquake. Less than a day after the 2010 Mw 8.8 Maule (Chile) earthquake, it peaked at 12 cm. Published coseismic finite-fault models for these events do not reproduce the measured SZU. One interpretation is that this SZU is universal, driven by volume deformation around the slab interface (van Dinther et al. 2019). In contrast, with synthetic tests and an investigation of the Maule event, we demonstrate the SZU may instead result from slip on the slab interface. Further, we suggest that slip occurs as rapid postseismic afterslip. We can reproduce the SZU with fault slip if elastic heterogeneities associated with the subducting slab are accounted for, as opposed to assuming homogeneous or layered elastic lithospheric structures.

The Seismometer to Investigate Ice and Ocean Structure (SIIOS) project is exploring the science capabilities of seismometers in ocean world analog environments. Ocean worlds, such as Europa, Enceladus and Titan, have thick global icy shells overlying liquid oceans. The icy shells may be seismically active due to tidal stresses. SIIOS tests several seismometers in a small-aperture array in a mock-lander configuration to quantify the ability to detect, locate, and identify seismic sources, as well as constrain local ice structure. The SIIOS experiment was deployed on two terrestrial analogs for ocean worlds. We first deployed on Gulkana Glacier in Alaska in September 2017, and then deployed in Northwestern Greenland, over a subglacial lake from May 2018-August 2018. Both areas serve as analog locations for Europa due to the layering of ice, water and rock. Gulkana was a relatively noisy site due to surface runoff and drainage, higher topographic variation (inducing rockfalls), and proximity to active plate boundaries. Greenland was a quieter site, in part due to its geologic setting high on the ice sheet, as well as from the installation process. During the Greenland deployment, we covered instruments with a large aluminum box that was buried, thus reducing noise from atmospheric and thermal effects. At both analog sites, the instruments passively recorded seismicity and seismic background noise. The passive data was used to create power spectral density (PSDs) and then probability density functions (PDFs), of the background noise. The PDFs of Gulkana showed higher noise levels compared to those of Greenland. Using the passive data, we detected and identified events originating from ice quakes, and in the case of Gulkana; rockfalls and drainage from a nearby moulin. A frequency-dependent polarization analysis was also conducted to indicate the dominant directionality of the background signals through time. The results indicate how background or ambient signals could be used on ocean worlds to characterize the local seismicity.

Anthropogenic aerosol emissions from North America and Europe have strong effects on the decadal variability of the West African monsoon. Anthropogenic aerosol effective radiative forcing is model dependent, but the impact of such uncertainty on the simulation of long-term West African monsoon variability is unknown. We use an ensemble of simulations with HadGEM3-GC3.1 that span the most recent estimates in simulated anthropogenic aerosol effective radiative forcing. We show that uncertainty in anthropogenic aerosol radiative forcing leads to significant uncertainty at simulating multi-decadal trends in West African precipitation. At the large scale, larger forcing leads to a larger decrease in the interhemispheric temperature gradients, in temperature over both the North Atlantic Ocean and northern Sahara. There are also differences in dynamic changes specific to the West African monsoon (locations of the Saharan heat low and African Easterly Jet, of the strength of the west African westerly jet, and of African Easterly Waves activity). We also assess effects on monsoon precipitation characteristics and temperature. We show that larger aerosol forcing results in a decrease of the number of rainy days and of heavy and extreme precipitation events and warm spells. However, simulated changes in onset and demise dates does not appear to be sensitive to the magnitude of aerosol forcing. Our results demonstrate the importance of reducing the uncertainty in anthropogenic aerosol forcing for understanding and predicting multi-decadal variability in the West African monsoon.

We consider approximately 32,000 microearthquakes that occurred between 2005 and 2016 in central Italy to investigate the crustal strength before and after the three largest earthquakes of the 2016 seismic sequence (i.e., the Mw 6.2, 24 August 2016 Amatrice, the Mw 6.1, 26 October 2016 Visso, and the Mw 6.5, 30 October 2016 Norcia earthquakes). We monitor the spatio-temporal deviations of the scaling between the seismic moment, M0, and the radiated energy, ES, with respect to a model calibrated for background seismicity. These deviations, defined here as Energy Index (EI), allow us to identify in the years following the Mw 6.1, 2009 L’Aquila earthquake a progressive evolution of the dynamic properties of the microearthquakes and the existence of high EI patches close to the Amatrice earthquake hypocenter. We show the existence of a crustal volume with high EI even before the Mw 6.5 Norcia earthquake. Our results agree with the suggested hypothesis that the Norcia earthquake nucleated at the boundary of a large patch that was highly stressed by the two previous mainshocks of the sequence. Furthermore, we highlight the mainshocks interaction both in terms of EI and of the mean loading shear stress associated to microearthquakes occurring within the crustal volumes comprising the mainshock hypocenters. Our study shows that the dynamic characteristics of microearthquakes can be exploited as beacons of stress change in the crust, and thus be exploited to monitor the seismic hazard of a region and help to intercept the preparation phase of large earthquakes.

Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many traditional dust emission models (TEMs) assume that the Earth’s land surface is devoid of vegetation, adjust dust emission using a vegetation cover complement, and calibrate the magnitude of modelled emissions to atmospheric dust. We compare this approach with a novel albedo-based dust emission model (AEM) which calibrates Earth’s land surface normalised shadow (1-albedo) to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. Existing datasets of satellite observed dust emission from point sources (DPS) and dust optical depth (DOD) show little spatial relation and DOD frequency exceeds DPS frequency by up to two orders of magnitude. Relative to DPS frequency, both dust emission models showed strong relations, but over-estimate dust emission frequency, suitable for calibration to observed dust emission. Our results show that TEMs over-estimate large dust emission over vast vegetated areas and produce considerable false change in dust emission, relative to the AEM. It is difficult to avoid the conclusion, raised by other literature, that calibrating dust cycle models to atmospheric dust has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without masks or vegetation cover. Considerable potential exists for Earth System Models driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

Although water availability strongly controls gross primary production (GPP), the impact of soil moisture content (wilting point) is poorly quantified on regional and global scales. In this study, we used 10-year observations of solar-induced chlorophyll fluorescence (SIF) from the GOSAT satellite to estimate the wilting point of a semiarid grassland on the Mongolian Plateau. Radiative-transfer model inversion and soil-vegetation-atmosphere transfer simulation were jointly conducted to distinguish the drought impacts on physiology from changes in leaf-canopy optical properties. We modified an existing inversion algorithm and the widely used SCOPE model to adequately evaluate dryland features, e.g., sparse canopy and strong convection. The modified model with retrieved parameters and calibrated to GOSAT SIF predicts realistic GPP values. We found that (1) the SIF yield estimated from GOSAT shows a clear sigmoidal pattern in relation to drought, and the estimated wilting point matches ground-based observations within ~0.01 m3 m-3 for the soil moisture content, (2) tuning the maximum carboxylation rate improves SIF prediction after considering changes in leaf-canopy optical properties, implying that GOSAT detected drought stress in leaf-level photosynthesis, and (3) the surface energy balance has significant impacts on the grassland’s SIF; the modified model reproduces observed SIF radiance well (mean bias = 0.004 mW m-2 nm-1 sr-1 in summer), whereas the original model predicts substantially low values under weak horizontal wind (unstable) conditions. Some model-observation mismatches in the SIF suggest that more research is needed for fluorescence parametrization (e.g., photoinhibition) and additional observation constraints.

Sediment-laden meltwater plumes are commonly observed in the fjords and coastal waters of Svalbard. Plumes are present at the margins of marine-terminating glaciers, and the mouths of glacier-fed subaerial rivers. They can influence fjord circulation, are useful proxies for meltwater runoff and can be used to infer a glacier’s hydrological system. Plumes often cover large areas of open water, meaning they are visible in satellite imagery. Passes by satellites provide an insight into what is occurring at a point in time, but satellite timeseries can also provide a longer temporal window than in-situ measurements. To determine how suspended sediment concentration (SSC) varies in plumes at two sites in Kongsfjorden, over three melt seasons (2012, 2019, 2021), we use in-situ and satellite-equivalent techniques. We find that SSC-reflectance relationships in a subaerial river plume are variable across three melt seasons, meaning relationships are not transferable from season-to-season. At a marine-terminating system, we find relationships remain stable across two melt seasons, however a small, non-significant dataset means there is low confidence in extrapolating these results. We also find that plumes from these subaerial and marine-terminating systems are not directly comparable due to differing geology and contrasting methods of meltwater delivery. Our findings suggest more research should be conducted into the relationship between SSC-reflectance at plumes in Svalbard, focussing on a system’s hydrology and lithology, whilst accounting for retreat at marine-terminating glaciers. Furthermore, new methods for interrogating cloud-impacted satellite scenes should be devised, reducing the reliance on short fieldwork windows for understanding plume variability.

A document by Nader Shakibay Senobari. Click on the document to view its contents.

A very close high-speed video observation of lightning attachment to a building revealed novel details regarding the leader streamer zone dynamics. Upward leaders propagate in a steady and unbranched manner, displaying a uniformly luminous corona brush. The exception being the upward connecting leader (UCL) just before connection, when its streamer zone increases in size and develops a more filamentary pattern. Downward negative leaders have 3-m long multiple streamers emanating from each negative leader tip. In some occasions, plasma formations known as space stems are seen to form in the location previously occupied by negative streamers. Space stems have luminosities comparable to the main leader channel, but are detached from it by 4 m. Some space stems display streamers of their own, including cases where streamers are emanating from both ends. The space stem formation hampered the propagation of the negative leader that was closest to the UCL.

Widespread valley networks (VNs) on Mars and other evidence point to an early warm and wet climate. However, ongoing debates still exist about VN’s formation processes and associated climatic conditions. The power law relationship between basin length and area (Hack’s Law) can be diagnostic of different fluvial processes related to climatic conditions. Past studies of Hack’s Law on Mars at local sites have produced inconclusive results.  Here we used a parameter-free method to delineate watersheds globally on Mars based on mapped VNs and extracted their Hack’s Law exponent (h). The majority of h values on Mars are similar to those in arid areas on Earth, suggesting similar runoff processes and arid conditions for VN formation on early Mars. Statistical analyses show that the spatial distribution of h on Mars is not random, but with a few clustered high and low values, likely controlled by local conditions (e.g., regional topographic slope).

Plant phenology regulates the carbon cycle and land-atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950-2014, 1981-2014, and 2015-2099, thermal-based indicators estimate spring advances of -0.7±0.2, -1.4±0.4, and -2.4±0.7 days/decade, while LAI-based indicators estimate -0.4±0.3, -0.1±0.3, and -1±1.1 days/decade. Thereby, LAI-based indicators exhibit later spring and weaker trends toward earlier onset, leading to index-related uncertainty being as large or larger than model uncertainty for a given index. Reconciling index-related discrepancies is therefore critical for understanding future changes in spring onset.

Climate change is both a physical and social phenomenon in which individual understandings are contextualized within broader considerations: individuals are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Personal experience, local knowledge, and social-learning influence climate risk perception and vary substantially among countries and regions. Likewise, they differently affect individuals and social groups at the regional and local scale, among whom exposures, attitudes, and capacities to manage risks vary greatly. A climate storyline approach is hence well-suited to study human observations, compound climate risks, and inform and conceptualize human–water systems interactions. Narrative storylines are used as input drivers to climate models, to represent different development pathways, which are usually characterized and applied at national and sub-national scales. Storylines aim to provide new social scenarios that address local human cognition uncertainties and improve human behavior modelling and robustness when addressing decision-making processes. Climate risks and hazards understanding can be communicated by presenting the experiences or a sequence of events, facts, and observations that are plausible and potentially critical for the system under study. Methods guiding storytelling are usually focused on conducting interviews with stakeholders, carrying out collective workshops, developing appropriate focal questions, and iterating between model results and key stakeholders. Therefore, can other data collection tools be used to reduce uncertainty in physical aspects of climate change from individuals’ local experience and perception? This contribution presents a triple-loop survey to detail the core elements of farmers’ perception and behavior when addressing climate change risk. We collect first-hand observations from northern Italian farmers about how climate change affects their activity and how extreme events are conditioning their adaptation capacity. Emphasis is placed on understanding the driving factors (risk awareness, perceived impacts, and adaptation measures and barriers) involved in the physically self-consistent past events and the plausibility of those factors. Moreover, we want to test if these factors can provide relevant implications for appropriately modelling storylines in decision-making processes. Tentative results can be useful to discuss the methodological framework of storylines building and narratives modelling, and at which point surveys can be an alternative and complementary way of dealing with deep uncertainty within climate risk management and social scenarios modelling.

Accurate prediction of precipitation intensity is of crucial importance for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is sub-grid scale cloud structure and organization, which affects precipitation intensity and stochasticity at the grid scale. Here we show, using storm-resolving climate simulations and machine learning, that by implicitly learning sub-grid organization, we can accurately predict precipitation variability and stochasticity with a low dimensional set of variables. Using a neural network to parameterize coarse-grained precipitation, we find mean precipitation is predictable from large scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ∼ 0.4) and underestimates precipitation extremes. Performance is significantly improved when the network is informed by our novel organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ∼ 0.95). The organization metric is implicitly learned by training the algorithm on high-resolution precipitable water, encoding organization degree and humidity amount at the subgrid-scale. The organization metric shows large hysteresis, emphasizing the role of memory created by sub-grid scale structures. We demonstrate this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing sub-grid scale convective organization in climate models to better project future changes in the water cycle and extremes.